FireWxNet: A Multi-Tiered Portable Wireless for Monitoring Weather Conditions in Wildland Fire Environments Paper By: Carl Hartung, Richard Han, Carl Selestad, Saxom Holbrook Presented by: D M Rasanjalee Himali

Wildland Fire Fighting Wildland firefighting is a dangerous, though necessary task during the summer months across the globe. Many firefighters fight these fires each year safety is the number one priority. Fire behavior can change rapidly due to a variety of environmental conditions: Ex: temperature, relative humidity, wind These environmental conditions can differ significantly between topographical features Ex: elevation,aspect. Hence, the ability to accurately monitor these environmental conditions over a wide area becomes very important.

Wildland Fire Fighting Predictions on fire behavior are usually based on a combination of : current observations,spot weather forecasts, recorded weather observations from the previous few days. Such predictions can give a general picture of expected fire behavior for a region But, actual fire behavior can vary tremendously over relatively small changes in elevation due to varying weather conditions. Ex: Thermal belts, Temperature Inversion The ability to detect thermal belts and inversions is of great importance to the fire community.

Wildland Fire Fighting Most common way of measuring weather on a fire is the use of a belt-weather kit Generally, one per squad carry such a kit, take measurements every hour or so, and report the data back to base camp. The base camp use this information to determine where to position units and when to pull them away from a fire. Drawbacks: only provides data for areas where squads are located. task can be easily forgotten when battling a fire

Wildland Fire Fighting The United States Forest Service (USFS) also maintains a network of around 2,200 permanent Remote Automated Weather Stations (RAWS) in different areas. They measure: temperature, wind, speed and direction,relative humidity, precipitation, barometric pressure, fuel moisture, soil moisture. Drawback: RAWS station are sparsely located. The positioning of stations is not always representative of the surrounding area.

FireWxNet: A Wireless Sensor Networks for Wildland fire Detection FireWxNet Design Goals: Weather Data report temperature, relative humidity, and wind speed and direction Visual Data have eyes on the fire 24 hours a day Elevational Gradient in Rugged Terrain provide data over a wide range of elevations in potentially extremely rugged mountainous and forested terrain. Long Range Remote Monitoring Transmit data upwards of 150 Km in order to relay information from the deployment areas to Incident Command Power Efficient network function for long periods of time Simple and Robust Simple to deploy and use. Continue to function in the presence of node failures Low Cost Portable

System Design and Implementation FireWxNet: a tiered system of wireless technologies The system needed to relay data from many points of interest to base camp (Incident Command) through an area with no internet connectivity or even electricity The satellite uplink at the base camp was used for internet access. The dish was then connected to backhaul network tier, a series of radios with directional antennas that created wireless links from 3-50 Km long Finally, at the end of each set of radios the weather network was connected. The weather network consisted of multiple sensor nodes with wireless links up to 400m as well as a steerable webcam.

Network Setup The deployment used: five long distance wireless links in backhaul, three sensor networks, and two web cameras. The cameras were set up at Hells Half Acre and Spot Mountain. The sensor networks set up at Hells Half Acre, Kit Carson, and Spot Mountain consisted of six nodes, five nodes, and two nodes respectively

Backhaul Network Tier For the main links two different types of radios made by TrangoBroadband Wireless was used: The Trango Access5830 strictly point-to-point directional radios achieve a range of roughly 50 kilometers. operated at 10 megabits per second in the frequency range of 900Mhz- 930Mhz. The Trango M900S Access Point / Subscriber Module Radios (AP/SU). used for shorter links radios formed a point-to-multipoint setup where the subscriber modules all communicated with a single access point. All the Trango radios used the standard and TCP/IP protocols for communication, and were manually given IP addresses prior to deployment.

Backhaul Network Tier At each hop, radios are connected to the next hop via an Ethernet switch. The Ethernet switches at each hop were Linksys WRTG45 4-port Wireless Access Point (WAP) switches. This meant that every radio hop in our network also provided standard WiFi internet access to any units in the area

Weather Network Hardware weather networks consisted of a number of sensor nodes, a webcam, and a small embedded computer running Linux. The webcams run their own web servers allowed users to connect to it from any web browser Both cameras provided an infra-red night vision feature and could deliver video at up to 30 frames per second

Base Station Base station provided the very important link between the sensor network and backhaul The device chosen for the base station is the Soekris net4801 The Soekris also boasts a CompactFLASH socket and a PCI slot. A stripped down version of Gentoo Linux was run from a 512Mb CompactFLASH card for operating system on the Soekris boards. The Soekris connected to the backbone through standard, wired ethernet.

Sensor Nodes The nodes in the senor network use Mica2 platfrom made by Xbow For communications, the Mica2 uses the Chipcon CC1000 radio operating at 900Mhz. For relative humidity sensor the Humirel 1520 RH sensor was used due to its superior accuracy at low relative humidity levels. The anemometer used is Davis Standard Anemometer. The anemometer provided wind direction accurate to within 7 degrees, and wind speed to within 5% of the reported value

Sensor Nodes While knowing the location of the nodes was very important FireWxNet sensor package does not have GPS units. This is because even small GPS units tend to use an enormous amount of energy, and would significantly decrease the life of our system. Since the nodes are immobile, hand held GPS unit was used to record the locations of the nodes at the time of deployment.

Sensor Nodes

Weather Network Software A robust mechanism was needed to ensure that data reach base stations even in the varying presence of interference and asynchronous links. Rather than implement a protocol with guaranteed delivery, a best-effort converge-cast protocol was developed. In this protocol, messages are sent multiple times for reliability. Therefore every packet need not reach the base station during a certain time period. Rather, only a single packet per node need to reach base station.

Weather Network Software The sensor network is built on the MANTIS operating system MANTIS is a multi-threaded, embedded operating system closely resembling Unix

Deployment Mechanism When powered on, the nodes would start by sending LOCATE packets at the rate of 1 packet per second. The nodes would also listen for LOCATE packets and respond with a similar FOUND packet. Each of the LOCATE and FOUND packets were the size of the largest data packet sent in the network. This was because smaller packets transmit further distances with less packet loss than larger packets.

Deployment Mechanism Once all of the nodes were placed, the base station was turned on and it began broadcasting control packets. All of the other nodes would forward the control packets using a standard flooding protocol. Upon receiving a control packet, nodes would begin their duty cycle

Routing When base station is powered on, it begins sending out control packets (or beacons) for one minute at the rate of one every four seconds. Beacons served multiple purposes: route discovery, fault tolerance, and time synchronization. Multiple beacons are sent during awake periods since network did not use any guaranteed delivery mechanisms. The beacons are propagated through the network by a simple directed flooding algorithm : nodes retransmit control packets when the distance to base (DTB) of the originating node is less (closer to base) than its own. When nodes sent data packets to the base station they used the same protocol in reverse. Data packets were forwarded only if the sending nodes DTB was greater than the receiver.

Duty Cycling and Time Synchronization In order to save power, entire network run on a duty cycle. Network has a 15 minute period where the nodes would sleep for 14 minutes, wake up and send packets for 1 minute, and then fall asleep again. Sleeping nodes would not forward packets Need to ensure that entire network run on the same 15 minute duty cycle. To accomplish this a loose, relative time synchronization mechanism is used

Duty Cycling and Time Synchronization Control beacons are used for time synchronization. Each beacon contained a sequence number which the nodes used to determine when the next sleep cycle would begin Once nodes awake, they wait to send any data until they hear a beacon. This mechanism keep network time synchronized with the base station at all times. Nodes resynchronize with the base every 15 minutes

Fault Tolerance Beacons provide fault tolerance During an awake period, the nodes listen for beacons from nodes with a DTB less than their own. If the nodes did not receive a beacon in 10 seconds (2 and 1/2 beacon cycles), they reset their own DTB and listen for any beacon. Upon hearing a new beacon the node would reconnect to the network with a new DTB. This mechanism allowed nodes to be reset, have their batteries replaced, create routes around failed nodes, or be moved and still continue to function in the network

Gathering Data To transmit all the data from the network to base camp, number of freely available tools are used. The data is gathered at the Soekris and written into a tab delineated text file. A cron-job run in the background which ftp-push that text file to a computer at Incident Command every 15 minutes just after each round of the network duty cycle. Alternatively, users could ssh or sftp into the Soekris and manually retrieve the logs at any time. Once at a local machine the data is generally imported into a spreadsheet or database program to be analyzed.

Network Performance Graphs show that: The lower elevation sensor nodes reported very large changes in temperature throughout the day The upper elevation nodes reported much smaller changes The temperature decreases as elevation increases (general case) An inversion set in each night around roughly 20:00 was observed and did not lift until 11:00-12:00 the next day

Network Performance Graphs show that: The lower elevation sensor nodes reported very large changes in temperature throughout the day The upper elevation nodes reported much smaller changes The temperature decreases as elevation increases (general case)